Adapting APC for the complexity of pulp and paper processes
One of the fundamental tools in any APC is model predictive control (MPC), which is a set of algorithms for feedback and feed-forward control based on a process model. While MPC has been around since the 1980s, it is relatively new for the pulp and paper industry - in large part due to the complexities of the process. Kraft pulping, for example, has many interacting processes, varying dynamics, and conflicting objectives, and is extremely difficult to model and control. It can also be easily disturbed by many factors such as raw material quality, equipment age and the ambient weather.
Over the years, ABB has developed additional tools to further optimize process automation and make APC into a more robust and adaptable form, leading to easier and more successful adoption for pulp and paper processes. Areas where ABB APC solutions have been successfully implemented include pulp digesters, brown stock washers, bleach plants, lime kilns, causticizers, and multi-effect evaporators as well as paper machines. Process variability can be significantly reduced, creating savings in energy and chemicals, and often reducing downtime and maintenance costs. In integrated pulp and paper mills, having APC modules in the pulp mill ensures a continuous stream of high-quality pulp to the paper machine. This makes the work of the paper machine controls easier and allows the mill to focus on cost reduction and efficiency improvement without having to worry about disturbances due to changes in pulp quality.
One critical development has been the inclusion of virtual measurements when physical measurements are infrequent or not available. Also known as soft sensors, these calculated measurements are particularly valuable in the pulp and paper industry where many processes are notoriously difficult to measure. Take for example our Kappa Virtual Measurement, which estimates Kappa in the cooking zone of a digester, where it is impossible to physically measure Kappa, providing more visibility into the pulp quality across the cooking plant. With more frequent Kappa measurements, the APC can make immediate corrections to create more even cooking. The results of these changes are also immediately visible in the calculations.
ABB offers additional tools to further optimize process automation, such as Pulp Tracking and Constraint Management. Pulp Tracking is a way to chart the movement of key pulp properties throughout the process, such as moisture content in wood chips, pulp conductivity in brown stock washers, or pulp brightness in bleaching. These variables are tracked in space and time up to the location of interest, with the insights used to build models for various APC modules across the mill. If off-spec pulp is produced for a short while at the digester, for example, the system can predict when this slug of pulp will reach the various bleaching stages and adjust bleaching parameters accordingly.
Constraint Management is a concept patented by ABB which dynamically calculates high and low limits for the APC variables. It uses tracking in coordination with predictive controls to help processes that are typically characterized by non-linearity, slow dynamics, and high interactions amongst key process variables. This approach ensures that the APC can adapt to varying process conditions and maintain tight control which otherwise wouldn’t be possible.